Collision avoidance capability is an essential component in an autonomous vessel navigation system. To this end, an accurate prediction of dynamic obstacle trajectories is vital. Traditional approaches to trajectory prediction face limitations in generalizability and often fail to account for the intentions of other vessels. While recent research has considered incorporating the intentions of dynamic obstacles, these efforts are typically based on the own-ship's interpretation of the situation. The current state-of-the-art in this area is a Dynamic Bayesian Network (DBN) model, which infers target vessel intentions by considering multiple underlying causes and allowing for different interpretations of the situation by different vessels. However, since its inception, there have not been any significant structural improvements to this model. In this paper, we propose enhancing the DBN model by incorporating considerations for grounding hazards and vessel waypoint information. The proposed model is validated using real vessel encounters extracted from historical Automatic Identification System (AIS) data.

翻译：碰撞规避能力是自主船舶导航系统的核心组成部分。为实现这一目标，对动态障碍物轨迹的准确预测至关重要。传统的轨迹预测方法在泛化能力方面存在局限，且往往未能考虑其他船舶的意图。尽管近期研究已尝试纳入动态障碍物的意图，但这些工作通常基于本船对态势的解读。该领域当前最先进的方法是动态贝叶斯网络（DBN）模型，该模型通过考虑多种潜在原因并允许不同船舶对态势存在不同解读，从而推断目标船舶的意图。然而，自该模型提出以来，其结构尚未出现重大改进。本文提出通过纳入搁浅风险考量与船舶航路点信息来增强DBN模型。所提模型使用从历史自动识别系统（AIS）数据中提取的真实船舶会遇场景进行了验证。